Add tests of is_numeric_dtype and is_integer_dtype and fix bugs.

python/cudf/cudf/api/types.py

@@ -3,6 +3,7 @@
 
 import datetime as dt
 from collections.abc import Sequence
+from inspect import isclass
 from numbers import Number
 
 import cupy as cp
@@ -16,6 +17,7 @@
 
 import cudf
 from cudf._lib.scalar import DeviceScalar
+from cudf.core.dtypes import _BaseDtype
 
 
 def is_categorical_dtype(obj):
@@ -92,13 +94,21 @@ def is_numeric_dtype(obj):
     bool
         Whether or not the array or dtype is of a numeric dtype.
     """
-    # TODO: we should handle objects with a `.dtype` attribute,
-    # e.g., arrays, here.
-    try:
-        dtype = np.dtype(obj)
-    except TypeError:
-        return False
-    return dtype.kind in "biuf"
+    if isclass(obj):
+        if issubclass(obj, cudf.Decimal64Dtype):
+            return True
+        if issubclass(obj, _BaseDtype):
+            return False
+    else:
+        if isinstance(obj, cudf.Decimal64Dtype) or isinstance(
+            getattr(obj, "dtype", None), cudf.Decimal64Dtype
+        ):
+            return True
+        if isinstance(obj, _BaseDtype) or isinstance(
+            getattr(obj, "dtype", None), _BaseDtype
+        ):
+            return False
+    return pd_types.is_numeric_dtype(obj)
 
 
 """
@@ -128,11 +138,13 @@ def is_integer_dtype(obj):
     bool
         Whether or not the array or dtype is of an integer dtype.
     """
-    try:
-        dtype = np.dtype(obj)
-    except TypeError:
-        return pd.api.types.is_integer_dtype(obj)
-    return dtype.kind in "iu"
+    if (
+        (isclass(obj) and issubclass(obj, _BaseDtype))
+        or isinstance(obj, _BaseDtype)
+        or isinstance(getattr(obj, "dtype", None), _BaseDtype)
+    ):
+        return False
+    return pd.api.types.is_integer_dtype(obj)
 
 
 def is_integer(obj):

python/cudf/cudf/tests/test_api_types.py

@@ -12,11 +12,13 @@
     "obj, expect",
     (
         (True, False),
+        (bool, False),
         (np.bool_, False),
         (np.int_, False),
         (np.float64, False),
         (complex, False),
         (np.complex128, False),
+        (bool(1), False),
         (np.bool_(1), False),
         (np.int_(1), False),
         (np.float64(1), False),
@@ -50,3 +52,95 @@
 )
 def test_is_categorical_dtype(obj, expect):
     assert types.is_categorical_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        (True, False),
+        (bool, True),
+        (np.bool_, True),
+        (np.int_, True),
+        (np.float64, True),
+        (complex, True),
+        (np.complex128, True),
+        (bool(1), False),
+        (np.bool_(1), True),
+        (np.int_(1), True),
+        (np.float64(1), True),
+        (complex(1), False),
+        (np.complex128(1), True),
+        (np.dtype("int"), True),
+        (np.dtype("float"), True),
+        (np.dtype("complex"), True),
+        (np.datetime64("2005-02-25T03:30"), False),
+        (cudf.Series(["2005-02-25T03:30"], dtype="datetime64[s]"), False),
+        (cudf.Series([1000], dtype="timedelta64[s]"), False),
+        (pd.Series(["2005-02-25T03:30"], dtype="datetime64[s]"), False),
+        (1, False),
+        (1.0, False),
+        ("hello", False),
+        (cudf.Series("hello"), False),
+        (cudf.Series("hello"), False),
+        (cudf.CategoricalDtype, False),
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.Series(["a"], dtype="category"), False),
+        (cudf.Series([1, 2], dtype=cudf.Decimal64Dtype(5, 2)), True),
+        (cudf.Decimal64Dtype(5, 2), True),
+        (cudf.Decimal64Dtype, True),
+        (pd.core.dtypes.dtypes.CategoricalDtypeType, False),
+        (pd.CategoricalDtype, False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.StructDtype, False),
+        (cudf.ListDtype, False),
+    ),
+)
+def test_is_numeric_dtype(obj, expect):
+    assert types.is_numeric_dtype(obj) == expect
+
+
+@pytest.mark.parametrize(
+    "obj, expect",
+    (
+        (True, False),
+        (bool, False),
+        (np.bool_, False),
+        (np.int_, True),
+        (np.float64, False),
+        (complex, False),
+        (np.complex128, False),
+        (bool(1), False),
+        (np.bool_(1), False),
+        (np.int_(1), True),
+        (np.float64(1), False),
+        (complex(1), False),
+        (np.complex128(1), False),
+        (np.dtype("int"), True),
+        (np.dtype("float"), False),
+        (np.dtype("complex"), False),
+        (np.datetime64("2005-02-25T03:30"), False),
+        (cudf.Series(["2005-02-25T03:30"], dtype="datetime64[s]"), False),
+        (cudf.Series([1000], dtype="timedelta64[s]"), False),
+        (pd.Series(["2005-02-25T03:30"], dtype="datetime64[s]"), False),
+        (1, False),
+        (1.0, False),
+        ("hello", False),
+        (cudf.Series("hello"), False),
+        (cudf.Series("hello"), False),
+        (cudf.CategoricalDtype, False),
+        (cudf.CategoricalDtype("a"), False),
+        (cudf.Series(["a"], dtype="category"), False),
+        (cudf.Series([1, 2], dtype=cudf.Decimal64Dtype(5, 2)), False),
+        (cudf.Decimal64Dtype(5, 2), False),
+        (cudf.Decimal64Dtype, False),
+        (pd.core.dtypes.dtypes.CategoricalDtypeType, False),
+        (pd.CategoricalDtype, False),
+        (cudf.Series([[1, 2], [3, 4, 5]]), False),
+        (cudf.Series([{"a": 1, "b": 2}, {"c": 3}]), False),
+        (cudf.StructDtype, False),
+        (cudf.ListDtype, False),
+    ),
+)
+def test_is_integer_dtype(obj, expect):
+    assert types.is_integer_dtype(obj) == expect